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Patient outcome prediction based on multi-omics data taking practitioners’ preferences into account. Calls prioritylasso::prioritylasso() from prioritylasso.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("regr.priority_lasso")
lrn("regr.priority_lasso")

Meta Information

  • Task type: “regr”

  • Predict Types: “response”

  • Feature Types: “logical”, “integer”, “numeric”

  • Required Packages: mlr3, prioritylasso

Parameters

IdTypeDefaultLevelsRange
blocksuntyped-
max.coefuntyped-
block1.penalizationlogicalTRUETRUE, FALSE-
lambda.typecharacterlambda.minlambda.min, lambda.1se-
standardizelogicalTRUETRUE, FALSE-
nfoldsinteger5\([1, \infty)\)
foldiduntyped-
cvoffsetlogicalFALSETRUE, FALSE-
cvoffsetnfoldsinteger10\([1, \infty)\)
handle.missingtestdatacharacter-none, omit.prediction, set.zero, impute.block-
include.allinterceptslogicalFALSETRUE, FALSE-
use.blocksuntypedall-
alignmentcharacterlambdalambda, fraction-
alphanumeric1\([0, 1]\)
bignumeric9.9e+35\((-\infty, \infty)\)
devmaxnumeric0.999\([0, 1]\)
dfmaxinteger-\([0, \infty)\)
epsnumeric1e-06\([0, 1]\)
epsnrnumeric1e-08\([0, 1]\)
excludeuntyped--
exmxnumeric250\((-\infty, \infty)\)
fdevnumeric1e-05\([0, 1]\)
gammauntyped--
groupedlogicalTRUETRUE, FALSE-
interceptlogicalTRUETRUE, FALSE-
keeplogicalFALSETRUE, FALSE-
lambdauntyped--
lambda.min.rationumeric-\([0, 1]\)
lower.limitsuntyped- , Inf-
maxitinteger100000\([1, \infty)\)
mnlaminteger5\([1, \infty)\)
mxitinteger100\([1, \infty)\)
mxitnrinteger25\([1, \infty)\)
nlambdainteger100\([1, \infty)\)
offsetuntyped-
parallellogicalFALSETRUE, FALSE-
penalty.factoruntyped--
pmaxinteger-\([0, \infty)\)
pminnumeric1e-09\([0, 1]\)
precnumeric1e-10\((-\infty, \infty)\)
standardize.responselogicalFALSETRUE, FALSE-
threshnumeric1e-07\([0, \infty)\)
trace.itinteger0\([0, 1]\)
type.gaussiancharacter-covariance, naive-
type.logisticcharacterNewtonNewton, modified.Newton-
type.multinomialcharacterungroupedungrouped, grouped-
upper.limitsuntypedInf-
scale.ylogicalFALSETRUE, FALSE-
return.xlogicalTRUETRUE, FALSE-
predict.gammanumericgamma.1se\((-\infty, \infty)\)
relaxlogicalFALSETRUE, FALSE-
snumericlambda.1se\([0, 1]\)

References

Simon K, Vindi J, Roman H, Tobias H, Anne-Laure B (2018). “Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data.” BMC Bioinformatics, 19. doi:10.1186/s12859-018-2344-6 .

See also

Author

HarutyunyanLiana

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrPriorityLasso

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.


Method selected_features()

Selected features when coef is positive

Usage

LearnerRegrPriorityLasso$selected_features()

Returns

character().


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrPriorityLasso$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("regr.priority_lasso")
print(learner)
#> <LearnerRegrPriorityLasso:regr.priority_lasso>: Priority Lasso
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, prioritylasso
#> * Predict Types:  [response]
#> * Feature Types: logical, integer, numeric
#> * Properties: selected_features, weights

# available parameters:
learner$param_set$ids()
#>  [1] "blocks"                 "max.coef"               "block1.penalization"   
#>  [4] "lambda.type"            "standardize"            "nfolds"                
#>  [7] "foldid"                 "cvoffset"               "cvoffsetnfolds"        
#> [10] "handle.missingtestdata" "include.allintercepts"  "use.blocks"            
#> [13] "alignment"              "alpha"                  "big"                   
#> [16] "devmax"                 "dfmax"                  "eps"                   
#> [19] "epsnr"                  "exclude"                "exmx"                  
#> [22] "fdev"                   "gamma"                  "grouped"               
#> [25] "intercept"              "keep"                   "lambda"                
#> [28] "lambda.min.ratio"       "lower.limits"           "maxit"                 
#> [31] "mnlam"                  "mxit"                   "mxitnr"                
#> [34] "nlambda"                "offset"                 "parallel"              
#> [37] "penalty.factor"         "pmax"                   "pmin"                  
#> [40] "prec"                   "standardize.response"   "thresh"                
#> [43] "trace.it"               "type.gaussian"          "type.logistic"         
#> [46] "type.multinomial"       "upper.limits"           "scale.y"               
#> [49] "return.x"               "predict.gamma"          "relax"                 
#> [52] "s"